How to Pick the BEST Stocks for Trading (with ZERO experience)

Finance-focused summary (stock trading system & criteria)

Core premise

• Day traders are “hunters of volatility and managers of risk.”
• The goal is to profit from intraday price swings, ideally around:
  • ~10% in ~15 minutes
• The speaker argues you don’t trade “all stocks”—on most days only 1–2 names move enough to matter.

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What types of stocks to trade

Avoid / be cautious

• Penny/OTC stocks
  • Often too volatile and frequently linked to pump-and-dump schemes.
• Stocks with extreme downside volatility
  • Example logic: if a stock is down ~50% in a day, it often implies elevated bankruptcy risk.
• Buyouts/mergers/acquisitions
  • Price can get “fixed,” often creating a sideways pattern like a barcode.
• Mergers / “headline mergers”
  • Can involve dilution or complex share structure.
• Shorting
  • Not recommended for beginners due to asymmetric risk.

Preferred

• Stocks moving upward with a real, understandable catalyst, such as:
  • Quarterly earnings
  • FDA approvals
  • Clinical trial results
  • New contracts (especially pharma/tech/A.I.-related catalysts)
• Technical-only moves can work too (e.g., breakouts or short squeezes), but the preference is for a fundamental catalyst when possible.

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Volatility threshold / market filtering

• Uses a “standard deviation intuition”:
  • If most stocks trade within roughly ±4% per day, they’re “not moving enough.”
• Main filter:
  • Focus on stocks up more than ~4%—especially the leading gainer of the day.
• Daily watch list workflow:
  • Thousands of stocks → ~30–40 → top 5–10 by biggest % gainer

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Price range “sweet spot”

• General preference: $1 to $20
• Most active zone: $1 to $10
  • Within that, $5 to $10 is described as the “peak” interest range.
• Frequency/probability tradeoffs:
  • $5–$10: more exciting setups, more trades
  • $1–$5: fewer trades, but still workable
  • $10–$20: traded as well, but expected trade frequency and profits decline
• Rationale:
  • Lower-priced stocks can make large percentage moves more easily (e.g., $3→$6 vs $30→$60).
  • Helps small accounts participate using share quantity.

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Catalyst validation via news

• After identifying a leading gainer, he checks the news window to confirm the catalyst.
• Caution if news is blank (“no discernable catalyst”):
  • Sometimes breakouts occur without headlines due to technical/momentum patterns, but he trades those with caution.

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Trading attention & broker incentives

• He claims retail brokers publicize leading gainers to encourage trading, citing payment for order flow.
• As a result, the biggest gainers can receive widespread attention (including mainstream spillover).

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Step-by-step framework (as described)

1. Daily scan (morning)

   • Run a scanner to find stocks up > ~4% (leading gainers).
   • Sort by change from close.
   • Narrow to ~30–40, then focus on top 5–10.

2. Select candidate using key filters

   • Prefer price $1–$20, especially $1–$10 (with $5–$10 as the “peak”).
   • Confirm catalyst when possible (earnings, FDA, clinical results, contracts).
   • Confirm volatility/risk profile is manageable (avoid extreme downside profiles).
   • Prefer relative volume (below).

3. Check liquidity/attention using volume metrics

   • He may look at volume, but the priority is big movers.
   • Use relative volume:
     • Prefer relative volume ≥ ~5x average
   • Supply/demand logic:
     • Big price moves typically come from high demand with limited supply (float).

4. Check “supply” using float / share availability

   • Prefer low float:
     • Typical cutoff: float < ~20M shares
     • In colder markets: tighten to ~10M
   • Example concept:
     • If float = 1.5M and volume ~12M, turnover is huge—often linked to larger gains.

5. Catalyst → then chart action

   • After catalyst checks, he focuses on chart/price action rather than being a deep fundamental analyst.
   • Claim: fundamentals matter more long-term; active trading is about intraday volatility.

6. Execution timing / “when to trade”

   • Typical trading window: ~7:00 a.m. to ~10:30–11:00 a.m. ET
   • Session context:
     • Regular hours: 9:30 a.m.–4:00 p.m. ET
     • Pre-market: ~4:00 a.m.–9:30 a.m. ET
     • After-hours: ~4:00–8:00 p.m. ET
   • He waits for broker liquidity (often ~7:00 a.m.).
   • Watches for headlines often around top/bottom of the hour, using audio alerts when criteria match.

7. Trade only when “momentum is present”

   • Emphasizes patience: don’t predict before it moves.
   • Success often comes from a small number of hot days (example given: ~80% of profits from a handful of days/month).

8. Avoid or minimize after-hours trading

   • After-hours is riskier due to low volume and reduced reliability once interest fades.
   • He mostly trades in the morning.

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Quantitative / key numbers and explicit recommendations

Performance/return comparisons

• Example target: ~10% in ~15 minutes
• Comparison to “average mutual fund”:
  • ~10% yearly (used to argue day trading is not directly comparable due to diversification differences)

Percent-change thresholds

• “Not interested” if within about ±4% standard deviation per day.
• Focus on:
  • Stocks up > 4% (scanner filter)
  • Prefer much larger gainers (mentions potential of 50%+)

Price range

• Main: $1–$20
• Sweet spot: $1–$10 (peak interest $5–$10)

Relative volume

• Prefer relative volume ≥ 5x average

Float (supply)

• Prefer float < ~20M, tighten to < ~10M in colder markets.
• Expect very large single-day % gains to correlate with low float.

Risk management via trade direction

• Strongly discourages short selling for beginners due to asymmetric risk.
• Short example:
  • Short at $20, shares 1,000
  • Max profit if stock goes to $0: $20k
  • If stock spikes to $500, loss becomes extreme:
    • about ~$480 loss per share → ~$480k on 1,000 shares
• Long-side argument:
  • Long losses are capped near 100% of position value if stock goes to $0, which he argues is easier to manage than large short-cover losses.

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Tickers / instruments mentioned

• cfrx (referenced multiple times)
• Tesla (TSLA) (momentum psychology example)
• Apple (AAPL)
• Netflix (NFLX)
• Google / Alphabet (GOOGL)
• Facebook / Meta (META)
• GameStop (GME) (short squeeze referenced)
• BFB (used in an example comparing lower vs higher nominal price stocks)
• Categories: OTC / penny stocks (not specific tickers)

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Disclosures / disclaimers

• No explicit “not financial advice” disclaimer appears in the provided subtitles.
• The speaker attributes the system to his own experience and claims a performance history:
  • “Less than $600 → more than $10 million” in “verified and audited trading profits.”

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Presenters / sources

• Presenter: Ross (full name not provided in the subtitles)
• Concepts/examples include market events such as:
  • GameStop short squeeze
  • Tesla momentum
• No external publication is specifically named.